Download the associated .kmz files to
open in Google Earth: VarroaGlobal,
Hawaii, Kenya
Use a high-speed internet connection.

Varroa
(Acari: Varroidae) is a parasitic mite that threatens the extinction of
the
world's honey bee (Apis mellifera) population.This mite not only feeds on bees and bee
larvae, but carries viral diseases and promotes stress to these
hard-working
insects (Sammataro et al., 2000).We
have been mapping the spread of this blight for quite awhile (Figure 1)
focusing on the importance of animation as a tool to draw together
space and
time.Understanding spatial pattern helps
to tighten focus on intervention.There
are obvious consequences associated with the possible extinction of
honey bees:
honey has long been an important agricultural crop (Ellis and Munn,
2005;
Matheson, 1996).In addition, honey bees
are important pollinators of one third of our crops, including fruits
and
vegetables and used in seed production (Free 1993; McGregor, 1976; http://www.sciencedaily.com/releases/2007/05/070510114621.htm).There are substantial economic implications
to the possible demise of bee pollinators as well as to the production
of honey,
long used as a natural sweetener (a healthy alternative to processed
sweeteners) and for medicinal purposes.The production of beeswax from the honeycomb is even more
valuable, a
primary foundation for cosmetics, as well as for making candles.All of these hive products have been
important since beekeeping was first recorded; wax and propolis
(bee-collected
plant resins) were vital to preserving Egyptian mummies.Beyond the obvious, when an established
species is removed from an ecosystem it is simple logic that the impact
of such
removal will have long-range, and perhaps unforeseen, consequences.

The
Varroa problem began in Asia in the early twentieth century (Goncalves
et al.
1985; Rosenkranz et al. 2010).Today,
Varroa is found worldwide, with some exceptions (Bradbear 1988;
Matheson 1996)
such as Australia. Erroneous
classification of the mite has clouded some of the reporting of
information.First identified as Varroa jacobsoni on the Asian honey bee Apis
cerana, molecular analysis has now separated
out four different Varroa species.We
refer here, for purposes of mapping, to the mite simply as "Varroa”,
and in
general terms it represents the new Varroa
destructor (Anderson and Trueman 2000) that jumped from the Asian
honey bee
onto the European honey bee (Apis
mellifera). Careful analysis of the
problem as a whole, beyond the tracking aspects, must consider the
taxonomic
problems as well (see Rosenkranz, et al., 2010; Navajas et al., 2010).

As
late as 2000, Varroa was discovered in New Zealand (Matheson 2000), in
Panama
(Calderon et al. 2000) and in St. Kitts & Nevis in the Caribbean.It has also been found in the Caribbean
islands of Grenada in 1994, Trinidad in 1996 (Hallim, M.K.I. 2000),
Cuba in
1996, Dominica in 1998, St. Lucia in 1999, Tobago and Nevis in 2000.It apparently has also been reported in Haiti
(dates forthcoming).On July 6, 2000,
Varroa was first detected in Panama.

The
recent discovery of Varroa mites in the Eastern Rift Valley in eastern
Kenya
(2009), the homeland of the honey bee species as well as a diverse
population
of wild (often unusual) animals, is particularly alarming because bees
and honey
are an integral part of subsistance-level farmers where honey is an
important
source of income. The discovery of mites somewhat earlier (2007) in the
tropical paradise of the remote Hawaiian Islands, will have a huge
impact since
many breeders raise queen honey bees there. The spread of these
mites can be directly
attributed to the movement of bee colonies by beekeepers and as well as
from some
hitchhiking bee swarms on ships. Other
mites are on the horizon which are equally devastating to bee
pollinators.

Current technology
permits far more than the basic mapping, by country, of Figure 1 which
is really not well-suited to showing small islands.
Improvement in technology to detect local locations in remote places,
using GPS or other mobile technology, requires mapping capability
beyond the traditional flat map. Adding local to global
information about pattern yields fuller insight
into spatial pattern and therefore into possible interventional
strategy.

The Case of Hawaii

Detailed maps, showing sightings (or
no sightings) of the mites in Hawaii can be fairly accurately
superimposed in Google Earth to take advantage of layering of
scientific maps, Google Earth aerials, and Google Earth Terrain.
Figures 2, 3, 4, and 5 all show how to achieve such layering for each
of four existing maps [Kunimoto]. The animations in these figures
begin and proceed as follows through to an end product that shows
superimposed Placemarks (yellow or red "balloons") representing an
inventory of selected locations and whether or not varroa was
present. As mapping has become more mobile, via laptops, smart
phones, and GPSs, the possibility of field-checking computer results in
the real world has become increasingly simplified. The mapping
steps are:

Find the general locale in Google Earth.

Add the existing map as an image overlay (it will not be
correctly aligned) (Kunimoto).

Set the level of opacity of the added map to about 50% so
that the map can be moved and stretched to fit, fairly well, the
underlying landmass in Google Earth.

While the opacity is still set at 50% add Placemarks (yellow
or red balloons, in this case) at the locations (yellow dots) indicated
on the maps.

Hide the added map so that only the native Google Earth
layer and the Placemarks show up.

Once the
locations are tied to the Google Earth base, then one can zoom in and
take a closer look, add other layers already present in Google Earth,
and generally take full advantage of the software capability (download the Hawaii
.kmz file and open it in Google Earth to look around). Such enhanced
3D visualization permits one to see the broad context of an actual
environment. The major
difficulty with taking a very close look, in the case of the Hawaii
data, is imperfect alignment of sightings, recorded on beautiful flat
maps, with the Google Earth
coordinate system. The circles on the added maps really only
suggest rough location and do not pinpoint location using latitude and
longitude. Further, there are small misalignments because flat
maps are stretched over the Google Globe (these are most evident in
Figure 3, where the map is stretched in an attempt to fit a number of
islands). Employing GPS
technology solves both problems. The Case of Kenya

In Kenya, the results of inventorying selected sites were recorded
using GPS coordinates (Figure 6). Thus, the precision in locating
them in Google Earth is greater than the precision used with the
Hawaiian data. However, the images available in Google Earth, and
related features, while helpful, are not as rich as those available for
Hawaii.

Figure
6. Locations in eastern Kenya, from GPS data.

The
available imagery, whatever it might be, is nontheless vastly superior
to what one might have found only a few years ago: it is in color
and it is easily available and free. It is most useful, however,
when other switches for other data already present in Google Earth are
used to supplement it. Figure 7 shows the UNEP inventory with a
sample from the Mount Kenya area of resolution higher than that of the
native imagery, coupled with roads and a page from Wikipedia.
Readers wishing to get the full effect should download the Kenya.kmz
file and view it in Google Earth.

Similarly,
Figures 8 and 9 illustrate other directions available to supplement
field evidence acquired using GPS technology and subsequently embedded
in Google Earth. Figure 8 delineates areas of change (three
areas) surrounding Mt. Kenya. Figure 9 gives the reader an idea
of the ruggedness of the terrain in the region.

Figure
8. Three areas of change (boxes outlined in yellow) in the region
surrounding Mt. Kenya.

Figure
9. Ruggedness of terrain in the area around Mt. Kenya.

In a
further similar survey (April-May, 2009) of 125 additional colonies
located in the eastern, western and coastal regions of Kenya (69
colonies in 18 locations), coastal Tanzania (18 colonies in 4 locations) including Ugunja and
Pemba Islands, collectively referred to as Zanzibar (likely A.m.
litorea), and Western Uganda (14 colonies in 4 locations), 87% of the
colonies tested positive for Varroa. Figure 10 is based on a map
in
Apidologie (Frazier, et al., 2010), subsequently translated to Google
Earth using that map as an image overlay. Locations read from
that map are coded as white balloons as it is not known precisely which
of them has colonies with varroa. In the animation of Figure 10,
the white balloons are displayed, as well, with the red ones from the
GPS survey of selected locations. Among the white balloons, only
the colonies surveyed in
western Uganda and two of the Zanzibar colonies tested negative for
mites. A limited survey of colonies in eastern Ghana (4 locations)
found low numbers of Varroa in 2 out of 12 colonies sampled, suggesting
that the mite has also spread to certain parts of West Africa.

The Global View, Revisited! Animation of maps is a powerful tool
for displaying spatial change over time. The simple layering of
flat maps, adjusting successive animation frame spacing to correspond
to real-world temporal spacing, can portray change effectively in a
single view (as in Figure 1). More recent technology, however,
permits the reader to do more than merely view the animation. The
embedded "tour" of Google Earth lets the reader interact with a 3D
display of Varroa distribution and experience directly the feeling of
movement of that spread. One can dive into the display and
see local imagery--stop the animation and extra navigation equipment
appears; try your mouse buttons in various ways.
Continue the animation after exploring a region; it will continue where
it left off. One can also portray both global data, as in
Figure 1, along with local data (as for Hawaii and Kenya in the other
figures
above) in a single
display. The capability to adjust the image permits the
simultaneous mapping of data at different scales. Further, the
"tour" aspect of the display in Figure 11 emphasizes
the global character of the distribution and helps to overcome the fact
that less than half the Google globe can be in view at any one
time. Test the interactive character of the imagery in Figure 11;
travel with Varroa!

The
mapping strategy employed for Varroa, now well-established, might work
equally well elsewhere. The small hive beetle is a new
player. Initial mapping efforts offer promising views of the
distribution of this pest (Neumann and Elzen, 2004; Neumann and Ellis,
2008). Perhaps the
day will come when the onboard data set of fine tools, such as Google
Earth has, will carry areas of change associated not only with
vegetation
and development issues, but also with changing wild life and
agricultural populations including even the humble, but important,
status of the honey bee!

Hallim,
M. K. I. 2000.Pests and diseases of honeybees in Trinidad and Tobago in the
year 2000
and recommendations to reduce their spread in the Caribbean.Paper presented to the Second Caribbean
Beekeeping
Congress, Nevis, August 14-18, 2000.Ministry of Agriculture Fisheries and Food, U.K.(1996) Varroosis
- a
parasitic infestation of honeybees.